|Table of Contents|

Testability Index Distribution Method Based on NSGA-Ⅱ Algorithm


Research Field:
Publishing date:


Testability Index Distribution Method Based on NSGA-Ⅱ Algorithm
ZHANG Qi1ZHU Chun-sheng1RAN Hong-liang2WANG Hai-tao2
1.Engineering Institute of Corps of Engineers,PLA University of Science & Technology,Nanjing 210007,China; 2.Department of Equipment and Transportation,Engineering Unversity of CAPF,Xi ’ an 710086,China
non-dominated sorting genetic algorithm Ⅱ multi-object optimization index distribution testability
In order to distribute testability indexes reasonably,considering the requirements of failure rate and fault influence severity for testability index,a multi-objective distribution model is proposed on the basis of analyzing the problem of testability index distribution.The optimal objectives of the model are the possibility functions of failure detection or failure isolation,the fault severity function and the life cycle cost function.The constraints are the testability requirements.The non-dominated sorting genetic algorithm Ⅱ is used to solve the multi-objective optimization model.Taking a hydraulic system of construction machinery as an example to validate the distribution model,and the results indicate that the method can distribute testability indexes reasonably and effectively.


[1] 田仲,石君友. 系统测试性设计分析与验证[M]. 北京: 北京航空航天大学出版社, 2003.
Tian Zhong, Shi Junyou. Analysis and verification of system design for testability [M]. Beijing: Beijing University of Aeronautics and Astronautics Press, 2003.
[2] 沈亲沐. 装备系统级测试性分配研究[D]. 长沙: 国防科学技术大学机电工程与自动化学院, 2007.
Shen Qinmu. Research on system-level testability allocation method for equipment[D]. National University of Defense Technology, 2007.
[3] 崔逊学. 多目标进化算法及其应用[M]. 北京: 国防工业出版社, 2008.
Cui Xunxue. Multi-objective evolutionary algorithms and their applications[M]. Beijing: National Defense Industry Press, 2008.
[4] Srinivas N,Deb K. Multi-objective optimization using non-dominated sorting in genetic algorithms[J]. Evolutionary Computation, 1994,2 : 221-248.
[5] Deb K,Agrawl S,Pratap A,et al. A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA Ⅱ[J]. Lecture Notes in Computer Science, 2000, 1917( 1) , 849-858.
[6] 李大南. 武器装备全寿命周期费用管理与标准化[J]. 航天标准化, 2008( 2) : 26-29.
 Li Danan. The management in the life cycle cost of weapon system equipment and standardization [J]. Aerospace Standardization, 2008( 2) : 26-29.
[7] 张永,吴晓蓓,徐志良,等. 基于Pareto 多目标遗传算法的模糊系统设计[J]. 南京理工大学学报, 2007, 31( 4) : 430-434.
 Zhang Yong,Wu Xiaobei,Xu Zhiliang, et al. Design of fuzzy systems based on Pareto multi-objective genetic algorithm[J]. Journal of Nanjing University of Science and Technology, 2007, 31( 4) : 430-434.
[8] 王跃宣,刘连臣,牟盛静,等. 处理带约束的多目标优化进化算法[J]. 清华大学学报( 自然科学版) , 2005, 45( 1) : 103-106.
 Wang Yuexuan,Liu Liancheng,Mu Shengjing,et al. Constrained multi-objective optimization evolutionary algorithm[J]. Journal of Tsinghua University( Science and Technology) , 2005, 45( 1) : 103-106.
[9] 钱伟懿,段红月. 具有约束多目标优化的进化算法[J]. 计算机应用与软件, 2010, 27( 2) : 115-117.
Qian Weiyi,Duan Hongyue. Evolutionary algorithm for multi-objective optimization problems with constraints [J]. Computer Applications and Software,2010,27 ( 2) : 115-117.
[10] Deb K,Pratap A,Meyarivan T. Constrained test problems for multi-objective evolutionary optimization [R]. KanGAL Report,Kanpur: Indian Institute of Technology, 2002.


Last Update: 2012-10-12